Tribe–charged system search for parameter configuration of nonlinear systems with large search domains

Talatahari, Siamak; Motamedi, Pouya; Azar, Bahman Farahmand; Azizi, Mahdi

doi:10.1080/0305215x.2019.1696786

Cited by 26 publications

(7 citation statements)

References 26 publications

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“…Cracking the parameter identification problem for various mathematical models can be addressed as one of the elaborate applications of optimization algorithms. [27] However, attaining an opposite and fitting arrangement of parameters that makes models achievable to estimate a remarkable approximation requires a proper objective function to be optimized. Accordingly, with the intention of evaluating α i and β i in Equations 5 and 6 using the parameter identification ability of optimization algorithms, the following objective function is employed, which is equal to the normalized mean square errors (NMSE) of data used for the regression model inference:…”

Section: Parameter Identification Problemmentioning

confidence: 99%

α_{i}

and

β_{i}

in Equations 5 and 6 using the parameter identification ability of optimization algorithms, the following objective function is employed, which is equal to the normalized mean square errors (NMSE) of data used for the regression model inference:

italicOF goodbreak= \frac{\frac{1}{n} \cdot \sum_{i = 1}^{n} {({\overset{Y}{true}}_{i} - Y_{i})}^{2}}{\frac{1}{n - 1} \cdot \sum_{i = 1}^{n} {({\overset{Y}{true}}_{i} - μ)}^{2}}

where

{\overset{⌢}{Y}}_{i}

denotes realization data extracted from the dataset of statistical values such as mean and standard deviations of

a_{i}

resulted in Bayesian regression inference of Equation 4,

Y_{i}

designated as the estimated results using Equations 5 and 6, and

μ

is the mean of actual data.…”

Section: Statement Of the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Seismic loss optimum design of steel structures using learning‐based charged system search

Motamedi

Banazadeh

Talatahari

2022

Structural Design Tall Build

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One of the structural engineers' challenges on a regular basis is balancing the expense of initial construction with the cost of future structural loss. At first appearance, employing the optimization method seems to be a viable option. However, since both the structure's analysis and design, as well as the computation of the cost of future loss, are time-consuming and expensive, combining these processes with the costly optimization progression is prohibitively expensive. The purpose of this study is to present a methodology for the risk-based optimum design of steel frame structures, as well as to enhance a previously published metaheuristic algorithm for a better optimization approach. The methodology given here allows structural designers to account for seismic risks in the design optimization process without incurring expensive computing expenditures. This approach may be appealing for practical work since it minimizes time-consuming charges and provides designers with a structural impression. Furthermore, as compared to the standard version, the new optimization algorithm improves performance while decreasing computing costs.Bayesian linear regression is used in conjunction with a parameter identification challenge to derive probabilistic models for estimating structural analysis demand responses. The minimum amount of total initial cost and seismic loss cost is regarded as the objective function of the design of three chosen mid-to high-rise moment frames for the optimization purpose. The results demonstrated enhanced optimization performance as well as a decreased loss cost for employed structures.

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Section: Parameter Identification Problemmentioning

confidence: 99%

α_{i}

and

β_{i}

italicOF goodbreak= \frac{\frac{1}{n} \cdot \sum_{i = 1}^{n} {({\overset{Y}{true}}_{i} - Y_{i})}^{2}}{\frac{1}{n - 1} \cdot \sum_{i = 1}^{n} {({\overset{Y}{true}}_{i} - μ)}^{2}}

where

{\overset{⌢}{Y}}_{i}

denotes realization data extracted from the dataset of statistical values such as mean and standard deviations of

a_{i}

resulted in Bayesian regression inference of Equation 4,

Y_{i}

designated as the estimated results using Equations 5 and 6, and

μ

is the mean of actual data.…”

Section: Statement Of the Problemmentioning

confidence: 99%

Seismic loss optimum design of steel structures using learning‐based charged system search

Motamedi

Banazadeh

Talatahari

2022

Structural Design Tall Build

Self Cite

View full text Add to dashboard Cite

show abstract

“…Genetic algorithms (GAs) [4,5] are based on the gradient-free approach that mimics evolution. Since then, several meta-heuristic nature-inspirated techniques have been formulated; these include particle swarm optimization (PSO) [6][7][8], evolutionary strategy (ES) [9], firefly algorithm (FA) [10], ant colony optimization (ACO) [11], differential evolution (DE) [12], probability-based incremental learning (PBIL) [13], big bang-big crunch algorithm [14], biogeography-based optimization (BBO) [15], harmony search (HS) [16], cuckoo search (CS) [17], animal migration optimization (AMO) [18], krill herd method (KH) [19], bat algorithm (BA) [20], teachinglearning-based optimization (TLBO) [21], and charged system search (CSS) [22]. KH is a modern swarm intelligence optimization technique inspired by krill herding behaviour [23].…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

et al. 2021

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Nowadays, solving constrained engineering problems related to optimization approaches is an attractive research topic. The chaotic krill herd approach is considered as one of most advanced optimization techniques. An advanced hybrid technique is exploited in this paper to solve the challenging problem of estimating the largest domain of attraction for nonlinear systems. Indeed, an intelligent methodology for the estimation of the largest stable equilibrium domain of attraction established on quadratic Lyapunov functions is developed. The designed technique aims at computing and characterizing a largest level set of a Lyapunov function that is included in a particular region, satisfying some hard and delicate algebraic constraints. The formulated optimization problem searches to solve a tangency constraint between the LF derivative sign and constraints on the level sets. Such formulation avoids possible dummy solutions for the nonlinear optimization solver. The analytical development of the solution exploits the Chebyshev chaotic map function that ensures high search space capabilities. The accuracy and efficiency of the chaotic krill herd technique has been evaluated by benchmark models of nonlinear systems. The optimization solution shows that the chaotic krill herd approach is effective in determining the largest estimate of the attraction domain. Moreover, since global optimality is needed for proper estimation, a bound type meta-heuristic optimization solver is implemented. In contrast to existing strategies, the synthesized technique can be exploited for both rational and polynomial Lyapunov functions. Moreover, it permits the exploitation of a chaotic operative optimization algorithm which guarantees converging to an expanded domain of attraction in an essentially restricted running time. The synthesized methodology is discussed, with several examples to illustrate the advantageous aspects of the designed approach.

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“…Highly nonlinear hysteresis of MRF damper behavior is one of the challenging aspects that need to be encountered to model their characteristics. In most cases, metaheuristic optimization methods are widely employed in parametric identification of highly nonlinear hysteretic of MRF damper [20]. The optimization is calculated by minimizing the error between the model outputs and the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models

et al. 2021

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The nonlinearity behaviour of magnetorheological fluid (MRF) can be described using a number of established models such as Bingham and Modified Bouc-Wen models. Since these models require the identification of model parameters, there is a need to estimate the parameters' value carefully. In this paper, an optimization algorithm, i.e., the Particle Swarm Optimization (PSO) algorithm, is utilized to identify the models' parameters. The PSO algorithm distinctively controls the best fit value by minimizing marginal error through root-mean-square error between the models and the empirical response. The validation of the algorithm is attained by comparing the resulting modified Bouc-Wen model behaviour using PSO against the same model's behaviour, identified using Genetic Algorithm (GA). The validation results indicate that the application of PSO is better in identifying the model parameters. Results from this estimation can be used to design a controller for various applications such as prosthetic limbs.

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Tribe–charged system search for parameter configuration of nonlinear systems with large search domains

Cited by 26 publications

References 26 publications

Seismic loss optimum design of steel structures using learning‐based charged system search

Seismic loss optimum design of steel structures using learning‐based charged system search

A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models

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